Consumer Confidence and Stock Markets: The Panel Causality Evidence
Bibliographic record
Abstract
This paper uses a panel of country-level data to investigate the causal relationship between the consumer confidence index (CCI) and the stock market index (SMI). We apply the common correlated effects mean group (CCEMG) estimation of Pesaran (2006) to capture the cross-sectional dependence of our variables before examining this causal relationship. In the panel data analysis, we discover the two-way causality between the CCI and SMI. One of the ways is where stock returns Granger-cause the changes in the CCI. According to the information view of the CCI, this result is due to consumers regarding the stock returns as being the leading indicators of the future situation, regardless of whether they own the stocks or not. On the other hand, the changes in the CCI also Granger-cause the stock returns, the reason for this being attributable to the animal spirits view of consumers. When consumers believe in their own opinions, they will at the same time have strong confidence in and an optimistic attitude toward the future economic situation. Based on these conditions, consumers will invest more in the stock market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".